'local models' 검색 결과

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Manage Ollama models and run text generation, chat, and embeddings, which represent text as numerical vectors, through command-line requests. Check server status, inspect model details, and receive structured JSON output for agent workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Download models and inspect their details
• Generate text or chat with a chosen model
• Convert text into numerical vectors

Guides experiments that inspect and change internal model values using nnsight. Covers local PyTorch models and remote experiments through NDIF, including replacing intermediate values to test how particular layers affect a model’s predictions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting layer outputs and attention patterns
• Replacing internal values to compare predictions
• Running large-model experiments through NDIF

Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a language model on a CPU-only computer
• Setting up local chat on an Apple Silicon Mac
• Choosing model quantization to fit available memory

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Explore local or remote repositories with Repomix CLI to understand their structure, locate related code across files, and review file counts and estimated tokens, the units AI models use to process text.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understanding an unfamiliar project's structure
• Locating authentication code and API endpoints
• Checking file counts and estimated AI tokens

Manage machine learning experiments with MLflow by recording settings, metrics, files, and trained models. Covers experiment comparison, model version management, reproducible project configurations, and deployment workflows across local and cloud environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording experiment settings and evaluation results
• Comparing model performance across experiments
• Managing model versions for production use

Create game images, transparent PNGs, textured 3D models, animated humanoids, sound effects, and voice lines using local GPU tools. Paid API options extend production to assets such as music and animated sprites.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating transparent character and item images
• Building textured 3D characters and props
• Adding character movement and lip-synced speech

Configure coding agents, AI SDKs, and HTTP clients to send model requests through AntSeed’s local proxy. Find available providers and models, select a provider, set compatible API endpoints, and verify request usage and payment activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect Claude Code or Codex to AntSeed
• Configure a local endpoint for AI SDKs
• Compare providers’ models, protocols, and prices

Reapply and repair Windows Codex Desktop after Store upgrades, including custom provider models hidden by Statsig available_models filtering, the dependent blue-purple Power slider and its Ultra toggle, Fast Mode request/UI gates, locale i18n, plugin UI gates, Chrome/browser_use gates, Goal command gates, Windows Computer Use availability gates and plugin/runtime repair, phone remote-control pairing under third-party/API-key main app usage, Desktop dynamicTools/inputSchema thread-start schema drift, local conversation visibility recovery after model_provider switches, restored-conversation missing-cwd continuation repair, ASAR integrity repair, signing/installing patched MSIX packages, SDK cleanup, Fast Mode wire verification, local plugin marketplace registration, and optional custom model_instructions_file setup (the bundled system prompt, also called 听话水).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run the ComfyUI agent locally for FREE with no subscription, no API key, and fully offline, using our gemma4 models fine-tuned on the comfyui-mcp tool suite via Ollama. Use when the user asks about running locally, running for free, offline use, avoiding API costs, Ollama setup, or which local model to pick.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

🇺🇸 Hardware + SaaS GTM Playbook — End-to-end go-to-market for consumer hardware products with software subscription models. Covers crowdfunding engineering (Kickstarter/Indiegogo/Makuake), KOL matrix for physical products, hardware PMF validation metrics, device-as-funnel subscription design, localization entry timing, and community flywheel mechanics. Built from verified Plaud, Insta360, and DJI case data. 🇨🇳 硬件+软件订阅 GTM 实操手册 — 面向消费硬件产品的完整出海及增长打法。覆盖众筹超额工程（KS/IGG/Makuake）、硬件 KOL 矩阵策略、硬件 PMF 验证、设备即漏斗订阅设计、本地化进入时机判断、社区飞轮构建。基于 Plaud/Insta360/大疆真实案例数据撰写。 🇯🇵 ハードウェア＋SaaS GTMプレイブック — クラウドファンディング（Kickstarter/IGG/Makuake）を起点とした消費者向けハードウェア製品のGTM戦略。KOLマトリクス、PMF検証、デバイスをファネルとするサブスクリプション設計、ローカライゼーション進入タイミング、コミュニティフライホイールまで網羅。Plaud・Insta360・DJIの実例データに基づく。 🇰🇷 하드웨어+SaaS GTM 플레이북 — 크라우드펀딩(Kickstarter/IGG/Makuake) 기반 소비자 하드웨어 제품의 엔드투엔드 GTM 전략. KOL 매트릭스, PMF 검증, 디바이스-as-퍼널 구독 설계, 현지화 진입 타이밍, 커뮤니티 플라이휠 포함. Plaud·Insta360·DJI 실제 사례 데이터 기반. Triggers: "hardware GTM" | "crowdfunding strategy" | "hardware+SaaS" | "Kickstarter launch" | "hardware KOL" | "hardware PMF" | "device subscription" | "hardware product launch" | "consumer hardware growth" | "Makuake" | "众筹策略" | "硬件出海" | "硬件+订阅" | "硬件GTM" | "KS众筹" | "硬件PMF" | "设备订阅" | "硬件冷启动" | "ハードウェアGTM" | "クラファン" | "하드웨어 GTM" | "크라우드펀딩"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Hands work to a local model served by LM Studio and verifies what comes back. Trigger to offload, delegate or batch work to a local, offline or on-device model; to queue unattended runs that keep only what passes; to ask which installed model fits a job or whether to switch; to size a task before delegating; when a local model returns nothing, repeats itself or drifts on a long list; when LM Studio shares the GPU with an image tool, or to free its hold; or say lmstudiorunner (audit, size, queue, run, status). Reads models from the live LM Studio API. Cloud model picks and prompt text are promptwright's; a schedule's guardrails agentwright's.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

sherpa-onnx 음성 모델로 컴퓨터에서 글을 낭독 음성으로 변환하고 WAV 파일로 저장합니다. 실행 환경과 모델을 준비하면 클라우드 연결 없이 음성을 생성합니다.

• 글을 WAV 음성 파일로 저장합니다
• 클라우드 연결 없이 음성을 만듭니다
• 모델을 바꿔 다른 목소리를 선택합니다

CodexBar의 로컬 비용 로그에서 Codex나 Claude의 비용을 모델별로 집계하고, 최근 대표 모델이나 전체 모델의 내역을 텍스트 또는 JSON으로 확인합니다.

• 최근 대표 모델의 비용을 확인합니다
• 전체 모델의 사용 비용을 비교합니다
• 내보낸 비용 로그를 집계합니다

Send research and analysis tasks to a running DeerFlow instance through its HTTP API, an interface for exchanging requests. Retrieve responses, continue conversations, upload documents, and inspect available models, skills, agents, and service health.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegate research and analysis to DeerFlow
• Upload documents for a conversation
• Resume an earlier conversation

Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating and submitting image generation workflows
• Checking or interrupting queued generation jobs
• Listing available models and node types

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Use Biohub’s Python SDK to complete protein sequences with ESM3, extract numerical sequence representations with ESMC, and predict all-atom structures with ESMFold2. The skill guides local or hosted inference, input validation, and output checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Completing masked positions in protein sequences
• Extracting numerical features from protein sequences
• Predicting all-atom protein structures

Use hosted DNA models to predict gene structures, expression levels, and regions that regulate gene activity. Start from a gene symbol, genomic coordinates, or a DNA sequence, without installing model weights or running a local GPU.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding promoter regions in a DNA sequence
• Predicting splice donor and acceptor sites
• Estimating gene expression for a specified cell type

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels